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    From 0 to 1 : Spark for Data Science with Python

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    From 0 to 1 : Spark for Data Science with Python - Online Course Download
    From 0 to 1 : Spark for Data Science with Python - Online Course Download
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    What you'll learn :

    Use Spark for a variety of analytics and Machine Learning tasks
    Implement complex algorithms like PageRank or Music Recommendations
    Work with a variety of datasets from Airline delays to Twitter, Web graphs, Social networks and Product Ratings
    Use all the different features and libraries of Spark : RDDs, Dataframes, Spark SQL, MLlib, Spark Streaming and GraphX

     

    Requirements :

    The course assumes knowledge of Python. You can write Python code directly in the PySpark shell. If you already have IPython Notebook installed, we’ll show you how to configure it for Spark
    For the Java section, we assume basic knowledge of Java. An IDE which supports Maven, like IntelliJ IDEA/Eclipse would be helpful
    All examples work with or without Hadoop. If you would like to use Spark with Hadoop, you’ll need to have Hadoop installed (either in pseudo-distributed or cluster mode).
     

    Description :

    Taught by a 4 person team including 2 Stanford-educated, ex-Googlers  and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with Java and with billions of rows of data. 

    Get your data to fly using Spark for analytics, machine learning and data science 

    Let’s parse that.

    What’s Spark? If you are an analyst or a data scientist, you’re used to having multiple systems for working with data. SQL, Python, R, Java, etc. With Spark, you have a single engine where you can explore and play with large amounts of data, run machine learning algorithms and then use the same system to productionize your code.

    Analytics: Using Spark and Python you can analyze and explore your data in an interactive environment with fast feedback. The course will show how to leverage the power of RDDs and Dataframes to manipulate data with ease. 

    Machine Learning and Data Science : Spark’s core functionality and built-in libraries make it easy to implement complex algorithms like Recommendations with very few lines of code. We’ll cover a variety of datasets and algorithms including PageRank, MapReduce and Graph datasets. 

    What’s Covered:

    Lot’s of cool stuff ..

    Music Recommendations using Alternating Least Squares and the Audioscrobbler dataset
    Dataframes and Spark SQL to work with Twitter data
    Using the PageRank algorithm with Google web graph dataset
    Using Spark Streaming for stream processing 
    Working with graph data using the  Marvel Social network dataset 

    .. and of course all the Spark basic and advanced features: 

    Resilient Distributed Datasets, Transformations (map, filter, flatMap), Actions (reduce, aggregate) 
    Pair RDDs , reduceByKey, combineByKey 
    Broadcast and Accumulator variables 
    Spark for MapReduce 
    The Java API for Spark 
    Spark SQL, Spark Streaming, MLlib and GraphFrames (GraphX for Python) 

    Who this course is for :

    Yep! Analysts who want to leverage Spark for analyzing interesting datasets
    Yep! Data Scientists who want a single engine for analyzing and modelling data as well as productionizing it.
    Yep! Engineers who want to use a distributed computing engine for batch or stream processing or both

     

    Course Size Details :

    8.5 hours on-demand video
    1 article
    2 downloadable resources
    Full lifetime access
    Access on mobile and TV
    Certificate of completion

    View Demo

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